Adaptive Threshold Method for Monitoring Rates in Public Health Surveillance

Simple item page
dc.contributor.authorGan, Linminen
dc.contributor.committeechairWoodall, William H.en
dc.contributor.committeememberReynolds, Marion R. Jr.en
dc.contributor.committeememberKim, Dong-Yunen
dc.contributor.committeememberLeman, Scotland C.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T21:10:47Zen
dc.date.adate2010-06-07en
dc.date.available2014-03-14T21:10:47Zen
dc.date.issued2010-04-30en
dc.date.rdate2010-06-07en
dc.date.sdate2010-05-04en
dc.description.abstractWe examine some of the methodologies implemented by the Centers for Disease Control and Prevention's (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS method W2 allows one to monitor syndrome counts (W2count) from each source and the proportion of counts of a particular syndrome relative to the total number of visits (W2rate). We investigate the performance of the W2r method designed using an empiric recurrence interval (RI) in this dissertation research. An adaptive threshold monitoring method is introduced based on fitting sample data to the underlying distributions, then converting the current value to a Z-score through a p-value. We compare the upper thresholds on the Z-scores required to obtain given values of the recurrence interval for different sets of parameter values. We then simulate one-week outbreaks in our data and calculate the proportion of times these methods correctly signal an outbreak using Shewhart and exponentially weighted moving average (EWMA) charts. Our results indicate the adaptive threshold method gives more consistent statistical performance across different parameter sets and amounts of baseline historical data used for computing the statistics. For the power analysis, the EWMA chart is superior to its Shewhart counterpart in nearly all cases, and the adaptive threshold method tends to outperform the W2 rate method. Two modified W2r methods proposed in the dissertation also tend to outperform the W2r method in terms of the RI threshold functions and in the power analysis.en
dc.description.degreePh. D.en
dc.identifier.otheretd-05042010-230757en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05042010-230757/en
dc.identifier.urihttp://hdl.handle.net/10919/37721en
dc.publisherVirginia Techen
dc.relation.haspartGan_L_D_2010.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectNegative binomial distributionen
dc.subjectOutbreak detectionen
dc.subjectRecurrence intervalen
dc.subjectExponentially weighted moving average charten
dc.subjectBiosurveillanceen
dc.titleAdaptive Threshold Method for Monitoring Rates in Public Health Surveillanceen
dc.typeDissertationen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Gan_L_D_2010.pdf
Size:
2.55 MB
Format:
Adobe Portable Document Format
Download

Collections

Doctoral Dissertations